Fast, reproducible, and portable software development environments

Remote development on HPC clusters with VSCode

Matrix multiplication example performed with OpenMP, OpenACC, BLAS, cuBLABS, and CUDA

Accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA tools and techniques.

nsight, an eclipse IDE for CUDA programming, set up for remote gpu server

A simple and understandable CUDA kernel for batch-matmul operation

The MNIST classification problem is a fundamental machine learning task that involves recognizing handwritten digits (0- 9) from a dataset of 70,000 grayscale images (28x28 pixels each). It serves as a benchmark for evaluating machine learning models, particularly neural networks.

Repository for Architecture of computers and parallel systems course on VŠB

learning how to do profiling on a yolov8 net using nvidia nsight compute

Custom PyTorch CUDA kernel implementing optimized ReLU activation with vectorization, performance profiling, and memory analysis on Tesla T4 GPU achieving 75% bandwidth efficiency.

University Project for "Computer Architecture" course (MSc Computer Engineering @ University of Pisa). Implementation of a Parallelized Nearest Neighbor Upscaler using CUDA.

🎬 Explore GPU training efficiency with FP32 vs FP16 in this modular lab, utilizing Tensor Core acceleration for deep learning insights.

A reproducible GPU benchmarking lab that compares FP16 vs FP32 training on MNIST using PyTorch, CuPy, and Nsight profiling tools. This project blends performance engineering with cinematic storytelling—featuring NVTX-tagged training loops, fused CuPy kernels, and a profiler-driven README that narrates the GPU’s inner workings frame by frame.

🚀 High-performance implementations and benchmarks of SSSP and APSP algorithms (Bellman–Ford, Dijkstra, Floyd–Warshall, Johnson) in Serial, OpenMP, CUDA, and Hybrid CPU+GPU. Includes profiling, speedup plots, and HPC notebooks